Recent works on fake news detection have shown the efficacy of using emotions as a feature or emotions-based features for improved performance. However, the impact of these emotion-guided features for fake news detection in cross-domain settings, where we face the problem of domain shift, is still largely unexplored. In this work, we evaluate the impact of emotion-guided features for cross-domain fake news detection, and further propose an emotion-guided, domain-adaptive approach using adversarial learning. We prove the efficacy of emotion-guided models in cross-domain settings for various combinations of source and target datasets from FakeNewsAMT, Celeb, Politifact and Gossipcop datasets.

翻译：最近关于假新闻探测的著作显示,将情感用作改善性能的特征或基于情感的特征是有效的,然而,这些情感引导功能在跨域环境中的假新闻探测中的影响基本上仍未被探索。 在这项工作中,我们评估了用于跨域假新闻探测的情感引导功能的影响,并进一步提议了一种使用对抗性学习的情感引导、域适应方法。我们证明了FakeNewsAMT、Celib、Politifact和Gossipcop数据集的各种源和目标数据集组合的跨域环境中情感引导模型的有效性。